202,142 research outputs found

    The Consensus Game: Language Model Generation via Equilibrium Search

    Full text link
    When applied to question answering and other text generation tasks, language models (LMs) may be queried generatively (by sampling answers from their output distribution) or discriminatively (by using them to score or rank a set of candidate outputs). These procedures sometimes yield very different predictions. How do we reconcile mutually incompatible scoring procedures to obtain coherent LM predictions? We introduce a new, a training-free, game-theoretic procedure for language model decoding. Our approach casts language model decoding as a regularized imperfect-information sequential signaling game - which we term the CONSENSUS GAME - in which a GENERATOR seeks to communicate an abstract correctness parameter using natural language sentences to a DISCRIMINATOR. We develop computational procedures for finding approximate equilibria of this game, resulting in a decoding algorithm we call EQUILIBRIUM-RANKING. Applied to a large number of tasks (including reading comprehension, commonsense reasoning, mathematical problem-solving, and dialog), EQUILIBRIUM-RANKING consistently, and sometimes substantially, improves performance over existing LM decoding procedures - on multiple benchmarks, we observe that applying EQUILIBRIUM-RANKING to LLaMA-7B outperforms the much larger LLaMA-65B and PaLM-540B models. These results highlight the promise of game-theoretic tools for addressing fundamental challenges of truthfulness and consistency in LMs

    Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

    Full text link
    Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture. In experiments using the TextWorld framework, we show that our proposed technique can learn a control policy faster than baseline alternatives. We have also open-sourced our code at https://github.com/rajammanabrolu/KG-DQN.Comment: Proceedings of NAACL-HLT 201

    Can You Explain That? Lucid Explanations Help Human-AI Collaborative Image Retrieval

    Full text link
    While there have been many proposals on making AI algorithms explainable, few have attempted to evaluate the impact of AI-generated explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG), as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA). In our proposed ExAG, a human user needs to guess a secret image picked by the VQA agent by asking natural language questions to it. We show that overall, when AI explains its answers, users succeed more often in guessing the secret image correctly. Notably, a few correct explanations can readily improve human performance when VQA answers are mostly incorrect as compared to no-explanation games. Furthermore, we also show that while explanations rated as "helpful" significantly improve human performance, "incorrect" and "unhelpful" explanations can degrade performance as compared to no-explanation games. Our experiments, therefore, demonstrate that ExAG is an effective means to evaluate the efficacy of AI-generated explanations on a human-AI collaborative task.Comment: 2019 AAAI Conference on Human Computation and Crowdsourcin

    Do neural-network question answering systems have a role to play in the deployment of information systems?

    Get PDF
    As Internet users become more numerous, experienced and skillful, and the number of companies doing ecommerce increases worldwide, so does the demand for online information about products and services. To satisfy this increasing demand for on-line information many companies have resorted to providing customer support services over a variety of on-line means of communication such as e.mail, chat services, voice on internet protocol (VOIP), etc. This article presents a stepwise approach to the construction of hybrid question answering systems based upon neural network technologies and natural language processing. These special kind of information systems not only provides high speed answers to questions posed by customers, but they also allow customers to receive answers to their questions on a 24/7 basis, provide well conceived standard answers to those questions, allow for a precise recording of customer communication, and make the management of customer support services easier. All of this is made clear by a case study about the development of an automatic question answering systems to the “SEBRAE Challenge”, a business game involving university students in seven different countries in South America
    • 

    corecore